"""
能耗分析服务模块
提供能耗数据收集、分析和优化建议功能
"""

from datetime import datetime, timedelta
from typing import List, Dict, Any, Tuple, Optional
from enum import Enum
import numpy as np
from uuid import uuid4
from collections import defaultdict

# 导入必要的模块
from schemas.energy_analysis import EnergyType, OptimizationLevel, EnergyEfficiencyGrade
from schemas.ai_algorithm import DetectionRequest, DetectionResponse, AIModelConfig, ModelPerformanceMetrics


# 简单的数据模型类
class EnergyData:
    def __init__(self, data_id: str, space_id: str, timestamp: datetime, energy_type: EnergyType, consumption: float):
        self.data_id = data_id
        self.space_id = space_id
        self.timestamp = timestamp
        self.energy_type = energy_type
        self.consumption = consumption


class EnergyAnalysisResult:
    def __init__(self, analysis_id: str, space_id: str, analysis_period: Tuple[datetime, datetime],
                 total_consumption: float, total_cost: float, consumption_by_type: Dict[str, float],
                 cost_by_type: Dict[str, float], efficiency_grade: EnergyEfficiencyGrade,
                 optimization_potential: Dict[str, float], analysis_timestamp: datetime):
        self.analysis_id = analysis_id
        self.space_id = space_id
        self.analysis_period = analysis_period
        self.total_consumption = total_consumption
        self.total_cost = total_cost
        self.consumption_by_type = consumption_by_type
        self.cost_by_type = cost_by_type
        self.efficiency_grade = efficiency_grade
        self.optimization_potential = optimization_potential
        self.analysis_timestamp = analysis_timestamp


class OptimizationRecommendation:
    def __init__(self, recommendation_id: str, space_id: str, energy_type: EnergyType,
                 optimization_level: OptimizationLevel, title: str, description: str,
                 estimated_savings: Dict[str, float], implementation_cost: float,
                 payback_period: int, priority: int, actions: List[Dict[str, str]]):
        self.recommendation_id = recommendation_id
        self.space_id = space_id
        self.energy_type = energy_type
        self.optimization_level = optimization_level
        self.title = title
        self.description = description
        self.estimated_savings = estimated_savings
        self.implementation_cost = implementation_cost
        self.payback_period = payback_period
        self.priority = priority
        self.actions = actions


class EnergyReport:
    def __init__(self, report_id: str, report_type: str, period: Tuple[datetime, datetime],
                 spaces: List[str], analysis_results: List[EnergyAnalysisResult],
                 recommendations: List[OptimizationRecommendation], summary: Dict[str, Any],
                 generated_at: datetime):
        self.report_id = report_id
        self.report_type = report_type
        self.period = period
        self.spaces = spaces
        self.analysis_results = analysis_results
        self.recommendations = recommendations
        self.summary = summary
        self.generated_at = generated_at



class EnergyAnalysisService:
    """能耗分析和优化服务"""
    
    def __init__(self):
        self.energy_data_storage = {}
        self.analysis_results_storage = {}
        self.recommendations_storage = {}
        self.reports_storage = {}
    
    async def collect_and_analyze_energy_data(self, space_id: str, energy_types: List[EnergyType],
                                            start_time: datetime, end_time: datetime) -> EnergyAnalysisResult:
        """收集和分析能耗数据"""
        # 模拟数据收集和分析
        analysis_result = EnergyAnalysisResult(
            analysis_id=str(uuid4()),
            space_id=space_id,
            analysis_period=(start_time, end_time),
            total_consumption=100.0,
            total_cost=60.0,
            consumption_by_type={energy_type.value: 25.0 for energy_type in energy_types},
            cost_by_type={energy_type.value: 15.0 for energy_type in energy_types},
            efficiency_grade=EnergyEfficiencyGrade.B,
            optimization_potential={energy_type.value: 10.0 for energy_type in energy_types},
            analysis_timestamp=datetime.now()
        )
        
        # 存储分析结果
        self.analysis_results_storage[analysis_result.analysis_id] = analysis_result
        
        return analysis_result
    
    def generate_optimization_recommendations(self, analysis_result: EnergyAnalysisResult) -> List[OptimizationRecommendation]:
        """生成优化建议"""
        recommendations = []
        
        for energy_type, potential in analysis_result.optimization_potential.items():
            if potential > 0:
                rec = OptimizationRecommendation(
                    recommendation_id=str(uuid4()),
                    space_id=analysis_result.space_id,
                    energy_type=EnergyType(energy_type),
                    optimization_level=OptimizationLevel.MEDIUM,
                    title=f"{energy_type}优化建议",
                    description=f"优化{energy_type}使用，预计节能{potential:.1f}kWh",
                    estimated_savings={
                        'energy_kwh': potential * 0.6,
                        'cost_yuan': potential * 0.6 * 0.6,
                        'co2_kg': potential * 0.6 * 0.785
                    },
                    implementation_cost=5000,
                    payback_period=12,
                    priority=2,
                    actions=[
                        {'type': 'optimize', 'description': f'优化{energy_type}设置'},
                        {'type': 'monitor', 'description': f'监控{energy_type}使用情况'}
                    ]
                )
                recommendations.append(rec)
        
        # 存储建议
        for rec in recommendations:
            self.recommendations_storage[rec.recommendation_id] = rec
        
        return recommendations
    
    def generate_energy_report(self, report_type: str, period: Tuple[datetime, datetime],
                             spaces: List[str]) -> EnergyReport:
        """生成能耗报告"""
        # 获取相关分析结果
        analysis_results = []
        for result in self.analysis_results_storage.values():
            if (result.space_id in spaces and 
                result.analysis_period[0] >= period[0] and 
                result.analysis_period[1] <= period[1]):
                analysis_results.append(result)
        
        # 获取相关建议
        recommendations = []
        for rec in self.recommendations_storage.values():
            if rec.space_id in spaces:
                recommendations.append(rec)
        
        # 生成报告摘要
        total_consumption = sum(result.total_consumption for result in analysis_results)
        total_cost = sum(result.total_cost for result in analysis_results)
        
        summary = {
            'period_type': report_type,
            'total_consumption_kwh': round(total_consumption, 2),
            'total_cost_yuan': round(total_cost, 2),
            'spaces_analyzed': len(analysis_results),
            'recommendations_count': len(recommendations),
            'potential_savings_kwh': sum(rec.estimated_savings.get('energy_kwh', 0) for rec in recommendations),
            'potential_savings_yuan': sum(rec.estimated_savings.get('cost_yuan', 0) for rec in recommendations)
        }
        
        # 生成报告
        report = EnergyReport(
            report_id=str(uuid4()),
            report_type=report_type,
            period=period,
            spaces=spaces,
            analysis_results=analysis_results,
            recommendations=recommendations,
            summary=summary,
            generated_at=datetime.now()
        )
        
        # 存储报告
        self.reports_storage[report.report_id] = report
        
        return report
    
    def get_energy_statistics(self, space_id: str, period: Tuple[datetime, datetime]) -> Dict[str, Any]:
        """获取能耗统计信息"""
        # 获取相关分析结果
        relevant_results = []
        for result in self.analysis_results_storage.values():
            if (result.space_id == space_id and 
                result.analysis_period[0] >= period[0] and 
                result.analysis_period[1] <= period[1]):
                relevant_results.append(result)
        
        if not relevant_results:
            return {'error': 'No data available for the specified period'}
        
        # 计算统计信息
        total_consumption = sum(result.total_consumption for result in relevant_results)
        total_cost = sum(result.total_cost for result in relevant_results)
        
        consumption_by_type = defaultdict(float)
        for result in relevant_results:
            for energy_type, consumption in result.consumption_by_type.items():
                consumption_by_type[energy_type] += consumption
        
        return {
            'space_id': space_id,
            'period': period,
            'total_consumption_kwh': round(total_consumption, 2),
            'total_cost_yuan': round(total_cost, 2),
            'consumption_by_type': dict(consumption_by_type),
            'analysis_count': len(relevant_results),
            'average_efficiency_grade': 'B'
        }


# 全局能耗分析服务实例
energy_analysis_service = EnergyAnalysisService()